import pandas as pd 
from datetime import datetime, timedelta
import warnings
import yfinance as yf
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta 
from scipy import stats
import matplotlib.dates as mdates
from matplotlib.colors import LinearSegmentedColormap
from plotly import tools
import plotly.tools as tls
import plotly.figure_factory as ff
import plotly.graph_objs as go
from plotly.subplots import make_subplots
from plotly.offline import init_notebook_mode, plot, iplot
import statsmodels.api as sm
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import adfuller
from sklearn.metrics import mean_squared_error
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

df1=pd.read_csv(r"C:\Users\PMLS\Downloads\sAND P TOP 5000 COAMPNIES NAMES\sp500_stocks.csv")
df1.head()

df1['Date'] = pd.to_datetime(df1['Date'])

now_time = datetime.now()
five_years_ago = now_time - timedelta(days=5*365)

df = df1[df1['Date'] >= five_years_ago]
df = df.reset_index(drop=True)  
df

# Ensure 'Date' column is in datetime format
df['Date'] = pd.to_datetime(df['Date'])

# Define the start and end dates
start_date = pd.to_datetime('2019-09-09')
end_date = pd.to_datetime('2024-09-04')

# Generate a date range from start_date to end_date
full_date_range = pd.date_range(start=start_date, end=end_date)

# Extract the unique dates from the 'Date' column in df_last_5_years
available_dates = pd.to_datetime(df['Date'].unique())

# Find the missing dates by comparing the full range with available dates
missing_dates = full_date_range.difference(available_dates)

# Get the count of missing dates
missing_count = len(missing_dates)

# Print the count of missing dates
print(f"Count of Missing Dates: {missing_count}")

# Print each missing date in a human-readable format
for missing_date in missing_dates:
    print(f"{missing_date.strftime('%d %B %Y')} is missing")d

df=df.rename(columns={"Symbol":"ticks"})
new_df= df.dropna()
new_df.isnull().sum()

Date         0
ticks        0
Adj Close    0
Close        0
High         0
Low          0
Open         0
Volume       0
dtype: int64

new_df

new_df.columns = new_df.columns.str.lower()

mean_dict = {}

# find average of volume traded over a period of time using for loops
for key in new_df['ticks'].unique():
    value = new_df[new_df['ticks'] == key ]['volume'].mean()
    mean_dict[key]= value

print("Length of the mean of ticks dictionary:", len(mean_dict))

# convert dict to pandas dataframe
avaerage_s = pd.Series(mean_dict)
top10_s = avaerage_s.sort_values(ascending=False)[:10]

print("Top 10 company tickers with highest average traded stock volume:\n", top10_s.index)

Length of the mean of ticks dictionary: 503
Top 10 company tickers with highest average traded stock volume:
 Index(['NVDA', 'TSLA', 'AAPL', 'AMZN', 'F', 'AMD', 'BAC', 'T', 'AAL', 'CCL'], dtype='object')

def subdataframe(df, tick):
    
    # top 10 ticks
    ticks = list(top10_s.index)
    
    assert tick in ticks, """Stock tick does not belong to top 10 stocks by trade volume, please try any of these:\n
    ['NVDA', 'TSLA', 'AAPL', 'AMZN', 'F', 'AMD', 'BAC', 'T', 'AAL', 'CCL']"""
    
    ndf = new_df[new_df['ticks'] == tick]
    return ndf

sp500Cmp_info_df = pd.read_csv('C:/Users/PMLS/Downloads/sAND P TOP 5000 COAMPNIES NAMES/sp500_companies.csv')
Top10HigestTradeNVD=['NVDA', 'TSLA', 'AAPL', 'AMZN', 'F', 'AMD', 'BAC', 'T', 'AAL', 'CCL']
info_df=pd.DataFrame({'tick':Top10HigestTradeNVD})
info_df

Final_info = pd.merge(info_df, sp500Cmp_info_df, how='left', left_on='tick', right_on='Symbol')
Final_info =Final_info[['tick','Shortname','Sector','Industry']]
Final_info

# Update company dict for your top 10 companies
company_dict = {
    'NVDA': 'NVIDIA', 
    'TSLA': 'Tesla', 
    'AAPL': 'Apple', 
    'AMZN': 'Amazon', 
    'F': 'Ford Motor Company', 
    'AMD': 'Advanced Micro Devices', 
    'BAC': 'Bank of America', 
    'T': 'AT&T', 
    'AAL': 'American Airlines', 
    'CCL': 'Carnival Corporation'
}

# Create sub-dataframes for each company
nvda_df = subdataframe(new_df, 'NVDA')
tsla_df = subdataframe(new_df, 'TSLA')
aapl_df = subdataframe(new_df, 'AAPL')
amzn_df = subdataframe(new_df, 'AMZN')
f_df = subdataframe(new_df, 'F')
amd_df = subdataframe(new_df, 'AMD')
bac_df = subdataframe(new_df, 'BAC')
t_df = subdataframe(new_df, 'T')
aal_df = subdataframe(new_df, 'AAL')
ccl_df = subdataframe(new_df, 'CCL')

# Define the function to calculate daily return and company name
def dailyfunc(df):
    df['daily return'] = ((df['close'] - df['open']) / df['open']) * 100
    df.style.format('{:.2f}%', subset='daily return')
    df['daily_mean'] = (df['open'] + df['close'] + df['high'] + df['low']) / 4
    df['co_name'] = company_dict[df['ticks'].unique()[0]]
    return df

# Apply dailyfunc to each company's dataframe
nvda_df = dailyfunc(nvda_df)
tsla_df = dailyfunc(tsla_df)
aapl_df = dailyfunc(aapl_df)
amzn_df = dailyfunc(amzn_df)
f_df = dailyfunc(f_df)
amd_df = dailyfunc(amd_df)
bac_df = dailyfunc(bac_df)
t_df = dailyfunc(t_df)
aal_df = dailyfunc(aal_df)
ccl_df = dailyfunc(ccl_df)

# Print the start and end date for each company One more for Confioatnos 
print('\t\tStart Date\t\t\t\t\tEnd Date')
print(f"NVDA\t\t{nvda_df['date'].min()}\t\t\t{nvda_df['date'].max()}")
print(f"TSLA\t\t{tsla_df['date'].min()}\t\t\t{tsla_df['date'].max()}")
print(f"AAPL\t\t{aapl_df['date'].min()}\t\t\t{aapl_df['date'].max()}")
print(f"AMZN\t\t{amzn_df['date'].min()}\t\t\t{amzn_df['date'].max()}")
print(f"F\t\t{f_df['date'].min()}\t\t\t{f_df['date'].max()}")
print(f"AMD\t\t{amd_df['date'].min()}\t\t\t{amd_df['date'].max()}")
print(f"BAC\t\t{bac_df['date'].min()}\t\t\t{bac_df['date'].max()}")
print(f"T\t\t{t_df['date'].min()}\t\t\t{t_df['date'].max()}")
print(f"AAL\t\t{aal_df['date'].min()}\t\t\t{aal_df['date'].max()}")
print(f"CCL\t\t{ccl_df['date'].min()}\t\t\t{ccl_df['date'].max()}")

		Start Date					End Date
NVDA		2019-10-01 00:00:00			2024-09-04 00:00:00
TSLA		2019-10-01 00:00:00			2024-09-04 00:00:00
AAPL		2019-10-01 00:00:00			2024-09-04 00:00:00
AMZN		2019-10-01 00:00:00			2024-09-04 00:00:00
F		2019-10-01 00:00:00			2024-09-04 00:00:00
AMD		2019-10-01 00:00:00			2024-09-04 00:00:00
BAC		2019-10-01 00:00:00			2024-09-04 00:00:00
T		2019-10-01 00:00:00			2024-09-04 00:00:00
AAL		2019-10-01 00:00:00			2024-09-04 00:00:00
CCL		2019-10-01 00:00:00			2024-09-04 00:00:00

def plot_closing_stock_prices(dfs, ncols=2):
    # Calculate the number of rows needed
    nrows = (len(dfs) + ncols - 1) // ncols
    
    # Create the figure and axes for the subplots
    fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(15, 5 * nrows), facecolor='#f4f4f4')
    fig.subplots_adjust(hspace=0.4, wspace=0.3)
    
    for ax, df in zip(axes.flat, dfs):
        # Calculate the highest stock price and the corresponding date
        high = df['close'].max()
        datetime = df[df['close'] == high]['date'].values[0]  

        # Define color based on the stock ticker
        if df['ticks'].unique()[0] in ['GE', 'F']:
            facecolor = '#ed615c'
        else:
            facecolor = '#4bd659'
        
        # Plot the data
        ax.plot(df['date'], df['close'], color='#0f2113')
        ax.set_title(f"{df['co_name'].unique()[0]} Stock Price", fontsize=16, fontweight='bold')
        ax.set_xlabel("Date", fontsize=12)
        ax.set_ylabel("Daily Closing Stock Price", fontsize=12)
        ax.set_facecolor('#ffffff')  # Set background color for each subplot
        
        # Add annotation for the highest stock price
        ax.annotate(
            f"All time high price during\nfive year period\nwas ${high:.2f}",
            xy=(datetime, high),
            xytext=(datetime, high - 0.1 * high),
            bbox=dict(boxstyle="round", facecolor='#f5d3bf', edgecolor='#d0d5db'),
            arrowprops=dict(facecolor='#f0190a', headlength=25, shrink=0.1)
        )

    # Hide any unused subplots
    for ax in axes.flat[len(dfs):]:
        ax.set_visible(False)

    plt.show()

# Call the function with your dataframes
plot_closing_stock_prices([nvda_df, tsla_df, aapl_df, amzn_df, f_df, amd_df, bac_df, t_df, aal_df, ccl_df])

def create_trade_volume_grid_single_row(dataframes):
    # Set the number of columns to be equal to the number of dataframes
    cols = len(dataframes)

    # Create subplot grid with 1 row and as many columns as there are dataframes
    fig = make_subplots(rows=1, cols=cols, subplot_titles=[df['co_name'].unique()[0] for df in dataframes])

    for i, df in enumerate(dataframes, 1):
        # All plots will be on row 1, so just adjust the column
        row = 1
        col = i

        # Calculate statistics
        ave_y = df['volume'].mean()
        max_y = df['volume'].max()
        min_y = df['volume'].min()
        max_date = df[df['volume'] == max_y]['date'].values[0]

        # Add trace for volume
        fig.add_trace(
            go.Scatter(x=df['date'], y=df['volume'], name='Volume', line=dict(color='#00CED1')),
            row=row, col=col
        )

        # Add horizontal lines for max, min, and average
        fig.add_hline(y=max_y, line_dash="dash", line_color="red", row=row, col=col)
        fig.add_hline(y=min_y, line_dash="dash", line_color="green", row=row, col=col)
        fig.add_hline(y=ave_y, line_dash="dash", line_color="yellow", row=row, col=col)

        # Add vertical line for max volume date
        fig.add_vline(x=max_date, line_dash="dash", line_color="purple", row=row, col=col)

        # Update axes
        fig.update_xaxes(title_text="Date", row=row, col=col)
        fig.update_yaxes(title_text="Volume", row=row, col=col)

    # Update layout
    fig.update_layout(height=300, width=400*cols, title_text="Stock Trade Volumes",
                      showlegend=False, template="plotly_dark")

    return fig

# Example usage
dataframes = [nvda_df, tsla_df, aapl_df, amzn_df, f_df, amd_df, bac_df, t_df, aal_df, ccl_df]
fig = create_trade_volume_grid_single_row(dataframes)
fig.show()

list_df = [nvda_df, tsla_df, aapl_df, amzn_df, f_df, amd_df, bac_df, t_df, aal_df, ccl_df]

# loop through the the list_df to find mini and maxi of each stocks 
mini = [df[df['date'] == df['date'].min()]['close'].values.item() for df in list_df]
maxi = [df[df['date'] == df['date'].max()]['close'].values.item() for df in list_df]

# find list of abosolute difference between both stock price
diff = np.array(maxi) - np.array(mini)

# find the percentage growth
growth = (diff/mini)*100
growth_list = growth.tolist()
co_name_list = [df['co_name'].unique()[0] for df in list_df]

# visualize the growth of the stocks
fig, ax = plt.subplots(figsize=(13,7))
ax.barh(y=co_name_list, width=growth_list, height=0.9, color=['#4bd659','#4bd659','#4bd659','#4bd659','#4bd659',
                                                             '#4bd659','#4bd659','#ed615c','#ed615c','#ed615c'],
       edgecolor='#713ae8')
for p in ax.patches:
    ax.annotate(f'{round(p.get_width(),2)}%', (p.get_width()+15, p.get_y() +0.3))
ax.set_xlabel('Percentage growth in stock price')
ax.set_ylabel('Name of companies')
ax.set_title("Growth in stock price over a period of 5 years")
plt.show()

# function to return top 10 sub dataframe
def subdataframe(df, tick):
    
    # top 10 ticks
    ticks = list(top10_s.index)
    
    assert tick in ticks, """Stock tick does not belong to top 10 stocks by trade volume, please try any of these:\n
    ['NVDA', 'TSLA', 'AAPL', 'AMZN', 'F', 'AMD', 'BAC', 'T', 'AAL', 'CCL']"""
    
    ndf = new_df[new_df['ticks'] == tick]
    return ndf

ab = sp500Cmp_info_df[sp500Cmp_info_df['Industry'] == 'Software - Infrastructure']
ab=ab.sort_values(by='Marketcap',ascending=False)
ab=ab.head(6)
ab

company_dict = {
    'MSFT': 'Microsoft',
    'ORCL': 'Oracle Corporation',
    'ADBE': 'Adobe Inc.',
    'PANW': 'Palo Alto Networks',
    'SNPS': 'Synopsys Inc.',
    'CRWD': 'CrowdStrike Holdings'
}

def subdataframe(df, tick, *ticks):
    ticks_list = list(ticks)
    assert tick in ticks_list, f"Stock tick does not belong to the provided list of ticks: {ticks_list}"
    ndf = df[df['ticks'] == tick]
    return ndf

TechStocks = ['MSFT', 'ORCL', 'ADBE', 'PANW', 'SNPS', 'CRWD']

# Create individual DataFrames for each stock
MSFT_df = subdataframe(new_df, 'MSFT', *TechStocks)
ORCL_df = subdataframe(new_df, 'ORCL', *TechStocks)
ADBE_df = subdataframe(new_df, 'ADBE', *TechStocks)
PANW_df = subdataframe(new_df, 'PANW', *TechStocks)
SNPS_df = subdataframe(new_df, 'SNPS', *TechStocks)
CRWD_df = subdataframe(new_df, 'CRWD', *TechStocks)

# Apply the dailyfunc function to each DataFrame
MSFT_df = dailyfunc(MSFT_df)
ORCL_df = dailyfunc(ORCL_df)
ADBE_df = dailyfunc(ADBE_df)
PANW_df = dailyfunc(PANW_df)
SNPS_df = dailyfunc(SNPS_df)
CRWD_df = dailyfunc(CRWD_df)

background_gradient = LinearSegmentedColormap.from_list("", ["#212121", "#1A1D23", "#03055B"])
line_colors = ['#FFFF00', '#00BFFF', '#32CD32', '#FF00FF', '#FFA500', '#d62728']  

# Create the figure and axis with dark background
fig, ax = plt.subplots(figsize=(14, 7))
fig.patch.set_facecolor('#1A1D23')  # Set figure background color to match theme
ax.set_facecolor('#212121')  # Set axis background color

# Plot each stock's daily mean price with distinct colors and line widths for visibility on dark theme
ax.plot(MSFT_df['date'], MSFT_df['daily_mean'], label='Microsoft (MSFT)', color=line_colors[0], lw=1.4)
ax.plot(ORCL_df['date'], ORCL_df['daily_mean'], label='Oracle (ORCL)', color=line_colors[1], lw=1.4)
ax.plot(ADBE_df['date'], ADBE_df['daily_mean'], label='Adobe (ADBE)', color=line_colors[2], lw=1.4)
ax.plot(PANW_df['date'], PANW_df['daily_mean'], label='Palo Alto Networks (PANW)', color=line_colors[3], lw=1.4)
ax.plot(SNPS_df['date'], SNPS_df['daily_mean'], label='Synopsys (SNPS)', color=line_colors[4], lw=1.4)
ax.plot(CRWD_df['date'], CRWD_df['daily_mean'], label='CrowdStrike (CRWD)', color=line_colors[5], lw=1.4)

# Format the x-axis to display years only
ax.xaxis.set_major_locator(mdates.YearLocator())  # Place tick at the start of each year
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))  # Format to show only the year
plt.xticks(rotation=0, color='white')  # Make tick labels white for dark background

# Add title and labels with improved formatting
ax.set_title("Comparative Analysis of Tech Stock Prices Based on Mean Price", fontsize=16, fontweight='bold', color='white')
ax.set_ylabel("Daily Average Stock Price", fontsize=14, color='white')

# Add legend with a clear background
ax.legend(facecolor='#1A1D23', fontsize="medium", title="Tech Stocks", title_fontsize=13, labelcolor='white', edgecolor='white')

# Add gridlines for better readability, change to a softer color to match the dark theme
ax.grid(True, color='#444444')

# Show the plot
plt.tight_layout()
plt.show()

dataframes = [MSFT_df, ORCL_df, ADBE_df, PANW_df, SNPS_df, CRWD_df]
labels = ['MSFT', 'ORCL', 'ADBE', 'PANW', 'SNPS', 'CRWD']

# Set up the plot
plt.figure(figsize=(14, 8))

# Loop through each DataFrame to plot only KDE (no histograms)
for df, label in zip(dataframes, labels):
    # Plot only KDE for each DataFrame's 'daily return' with a specific color palette for differentiation
    sns.kdeplot(df['daily return'], label=label, linewidth=2)

# Add labels and title
plt.title('KDE of Daily Returns for Top 6 Stocks')
plt.xlabel('Daily Return')
plt.ylabel('Density')
plt.legend(title='Stock')

# Show the plot
plt.show()

dataframes = [MSFT_df, ORCL_df, ADBE_df, PANW_df, SNPS_df, CRWD_df]
labels = ['MSFT', 'ORCL', 'ADBE', 'PANW', 'SNPS', 'CRWD']

# Create a combined DataFrame for plotting
combined_df = pd.DataFrame()

# Loop through dataframes and calculate standard deviation of 'daily return'
std_devs = {}
for df, label in zip(dataframes, labels):
    # Create a temporary DataFrame with a 'Stock' column
    temp_df = df[['daily return']].copy()
    temp_df['Stock'] = label
    
    # Append to the combined DataFrame
    combined_df = pd.concat([combined_df, temp_df])
    
    # Calculate the standard deviation for this stock and store it
    std_devs[label] = temp_df['daily return'].std()

# Sort stocks by their standard deviation (volatility)
sorted_labels = sorted(std_devs, key=std_devs.get)

# Set up the plot
plt.figure(figsize=(12, 6))

# Create the box plot, sorted by volatility
sns.boxplot(x='Stock', y='daily return', data=combined_df, order=sorted_labels, palette="Set2")

# Add labels and title
plt.title('Box Plot of Daily Returns for Top 6 Stocks (Sorted by Volatility)')
plt.ylabel('Daily Return')

# Show the plot
plt.show()

means = []
std_devs = []

# Loop through dataframes and calculate mean and standard deviation for each stock
for df in dataframes:
    mean_return = df['daily return'].mean()
    std_dev = df['daily return'].std()
    means.append(mean_return)
    std_devs.append(std_dev)

# Define size and color for scatter plot
area = np.pi * 20  # Size of the scatter plot marker
colors = ['blue', 'green', 'red', 'purple', 'orange', 'brown']  # Colors for each stock

# Create a scatter plot
plt.figure(figsize=(12, 8))

# Plot each stock with a different color and annotate
for mean, std, label, color in zip(means, std_devs, labels, colors):
    plt.scatter(mean, std, s=area, c=color, label=label)
    plt.annotate(label, xy=(mean, std), xytext=(50, 50), textcoords='offset points',
                 ha='right', va='bottom', 
                 arrowprops=dict(arrowstyle='-', color=color, connectionstyle='arc3,rad=-0.3'))

# Add labels, title, and legend
plt.xlabel('Expected Return (Mean)')
plt.ylabel('Risk (Standard Deviation)')
plt.title('Scatter Plot of Risk vs Expected Return for Top 6 Stocks')

# Show the plot
plt.show()

dataframes = [PANW_df, CRWD_df]
labels = ['PANW', 'CRWD']

PANW_df['date'] = pd.to_datetime(PANW_df['date'])
CRWD_df['date'] = pd.to_datetime(CRWD_df['date'])
PANW_df.set_index('date', inplace=True)
CRWD_df.set_index('date', inplace=True)

short_window = 50
long_window = 200

# Loop through companies and calculate moving averages
for company, label in zip([PANW_df, CRWD_df], ['PANW', 'CRWD']):
    company['Short MA'] = company['adj close'].rolling(window=short_window).mean()
    company['Long MA'] = company['adj close'].rolling(window=long_window).mean()
    
    # Detect Golden Cross and Death Cross
    company['Signal'] = 0.0  # Initialize signal column
    company['Signal'] = np.where(company['Short MA'] > company['Long MA'], 1.0, 0.0)
    company['Cross'] = company['Signal'].diff()

# Function to plot stock with Golden Cross and Death Cross
def plot_stock_with_cross(company, name, ax):
    ax.plot(company['adj close'], label='Adjusted Close', alpha=0.6)
    ax.plot(company['Short MA'], label=f'{short_window}-day MA', alpha=0.7)
    ax.plot(company['Long MA'], label=f'{long_window}-day MA', alpha=0.7)
    
    # Mark Golden Cross (1) and Death Cross (-1)
    ax.plot(company[company['Cross'] == 1].index, company['Short MA'][company['Cross'] == 1], '^', markersize=10, color='g', lw=0, label='Golden Cross')
    ax.plot(company[company['Cross'] == -1].index, company['Short MA'][company['Cross'] == -1], 'v', markersize=10, color='r', lw=0, label='Death Cross')
    
    ax.set_title(name)
    ax.legend()

    # Set major ticks to every year (from 2019 to 2024)
    ax.xaxis.set_major_locator(mdates.YearLocator())  # Every year
    ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))  # Format as 'Year'
    
    # Ensure x-axis spans from 2019 to 2024
    ax.set_xlim([pd.Timestamp('2019-01-01'), pd.Timestamp('2024-12-31')])

# Set up plot with subplots for PANW and CRWD
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 6))

# Plot for PANW and CRWD
plot_stock_with_cross(PANW_df, 'PANW', axes[0])
plot_stock_with_cross(CRWD_df, 'CRWD', axes[1])

plt.tight_layout()
plt.show()

check = CRWD_df[CRWD_df['Cross'] == -1]
check

symbols = ['GOOGL', 'MSFT','WMT','AMZN','BABA','ORCL','SAP','CRM']

holders_data = []
for symbol in symbols:
    stock = yf.Ticker(symbol)
    try:
        institutional_holders = stock.institutional_holders
        holders_data.append({
            'Stock Symbol': symbol,
            'Top 10 Institutional Holders': institutional_holders.head(10)
        })
    except:
        holders_data.append({
            'Stock Symbol': symbol,
            'Top 10 Institutional Holders': None
        })

Holders_data_df = pd.DataFrame(holders_data)
Holders_data_df.set_index('Stock Symbol', inplace=True)
Holders_data_df

from plotly.subplots import make_subplots

def create_interactive_holdings_pie_chart(df):
    # Create a 4x2 subplot grid
    fig = make_subplots(rows=4, cols=2, subplot_titles=df.index, specs=[[{'type':'domain'}, {'type':'domain'}]] * 4)

    for i, (symbol, data) in enumerate(df.iterrows(), 1):
        row = (i - 1) // 2 + 1
        col = (i - 1) % 2 + 1

        if data['Top 10 Institutional Holders'] is not None:
            holders = data['Top 10 Institutional Holders']
            
            # Create a pie chart for each stock
            fig.add_trace(
                go.Pie(
                    labels=holders['Holder'],
                    values=holders['Shares'],
                    name=symbol,
                    textposition='inside',
                    textinfo='percent+label',
                    hoverinfo='label+value+percent',
                    textfont_size=8
                ),
                row=row, col=col
            )

    # Update layout
    fig.update_layout(
        height=2000, 
        width=1400,
        title_text="Top 10 Institutional Holders for Various Stocks",
        showlegend=False
    )

    return fig

# Assuming Holders_data_df is your DataFrame with the data
# Create the plot
fig = create_interactive_holdings_pie_chart(Holders_data_df)

# Show the plot
fig.show()

symbols = ['GOOGL', 'MSFT','WMT','AMZN','BABA','ORCL','SAP','CRM']

file_paths = {
    'AMD': r"C:\Users\PMLS\Desktop\MicroTrends _Stock_info\AMD PE Ratio 2010-2024.txt",
    'Apple': r"C:\Users\PMLS\Desktop\MicroTrends _Stock_info\Apple PE Ratio 2010-2024.txt",
    'Microsoft': r"C:\Users\PMLS\Desktop\MicroTrends _Stock_info\Microsoft PE Ratio 2010-2024.txt",
    'NVIDIA': r"C:\Users\PMLS\Desktop\MicroTrends _Stock_info\NVIDIA PE Ratio 2010-2024.txt"
}

def clean_eps(value):
    if pd.isna(value) or value == '':
        return None
    return float(value.replace('$', ''))

dfs = {}

for stock, file_path in file_paths.items():
    df = pd.read_csv(file_path, sep=r'\s+', header=None, names=['Date', 'Price', 'EPS', 'PE ratio'])
    
    df['EPS'] = df['EPS'].apply(clean_eps)
    
    df['Price'] = pd.to_numeric(df['Price'], errors='coerce')
    df['PE ratio'] = pd.to_numeric(df['PE ratio'], errors='coerce')
    
    df['Date'] = pd.to_datetime(df['Date'])
    df.set_index('Date', inplace=True)
    
    df = df.iloc[1:]
    
    df.sort_index(inplace=True, ascending=False)
    
    dfs[stock] = df

# Extract the EPS columns from the DataFrames
eps_values = [df['EPS'] for df in dfs.values()]

fig = go.Figure()

# Add lines for each stock
for stock, eps in zip(dfs.keys(), eps_values):
    fig.add_trace(go.Scatter(x=eps.index, y=eps.values, name=stock, line=dict(width=2)))

# Set title and labels
fig.update_layout(
    title='EPS Over Time',
    xaxis_title='Date',
    yaxis_title='EPS',
    plot_bgcolor='#333333',
    paper_bgcolor='#333333',
    font_color='#ffffff'
)

fig.update_xaxes(showgrid=False)
fig.update_yaxes(showgrid=False)

# Set vibrant line colors
fig.update_traces(marker=dict(color=['#ff69b4', '#33cc33', '#6666ff', '#ffcc00']))

# Show the legend
fig.update_layout(showlegend=True)

fig.update_layout(
    width=600,  # Set the figure width (in pixels)
    height=300,  # Set the figure height (in pixels)
)

# Show the plot
fig.show()

pe_ratios = [df['PE ratio'] for df in dfs.values()]
stock_symbols = list(dfs.keys())

# Create the figure
fig = go.Figure()

# Add lines for all stocks, using the stock symbols from the dictionary keys
for i, stock in enumerate(stock_symbols):
    fig.add_trace(go.Scatter(x=pe_ratios[i].index, y=pe_ratios[i].values, name=stock, line=dict(width=2), visible=(i < 2)))

# Set title and labels
fig.update_layout(
    title='PE Ratio Over Time',
    xaxis_title='Date',
    yaxis_title='PE Ratio',
    plot_bgcolor='#333333',
    paper_bgcolor='#333333',
    font_color='#ffffff'
)

# Set vibrant line colors
fig.update_traces(marker=dict(color=['#ff69b4', '#33cc33', '#6666ff', '#ffcc00']))
fig.update_xaxes(showgrid=False)
fig.update_yaxes(showgrid=False)
# Add dropdown menu
buttons = [
    dict(label=f'{stock_symbols[0]} & {stock_symbols[3]}', method='update', args=[{'visible': [True, False, False, True]}]),
    dict(label=f'{stock_symbols[1]} & {stock_symbols[2]}', method='update', args=[{'visible': [False, True, True, False]}]),
    dict(label='All 4', method='update', args=[{'visible': [True, True, True, True]}])
]
fig.update_layout(updatemenus=[dict(buttons=buttons, direction='down', showactive=True)])

# Show the legend
fig.update_layout(showlegend=True)

# Show the plot
fig.show()

tickers = {
    'Cybersecurity': ['PANW', 'CRWD', 'FTNT'],
    'E-commerce': ['ETSY', 'SHOP', 'W'],
    'Fintech': ['SQ', 'PYPL', 'V'],
    'Software': ['TEAM', 'ZM', 'MSFT'],
    'Gaming': ['EA', 'TTWO', 'AIY.DE'],
    'Automotive': ['RIVN', 'MBLY', 'VFS','TSLA']
}

def get_data(ticker):
    try:
        ticker_obj = yf.Ticker(ticker)
        info = ticker_obj.info
        financials = ticker_obj.financials
        balance_sheet = ticker_obj.balance_sheet
        cash_flow = ticker_obj.cashflow
        
        if financials.empty or balance_sheet.empty or cash_flow.empty:
            print(f"Warning: Missing data for {ticker}")
            return None, None, None, None, None
        
        return ticker_obj, info, financials, balance_sheet, cash_flow
    
    except Exception as e:
        print(f"Error fetching data for {ticker}: {str(e)}")
        return None, None, None, None, None

def calculate_pe(info):
    pe = info.get('trailingPE', 0) or 0
    return min(max(pe, 0), 100)  # Cap PE ratio between 0 and 100

def get_value(df, possible_names, year, ticker):
    for name in possible_names:
        if name in df.index and year in df.columns:
            return df.loc[name, year]
    print(f"Warning: Could not find any of {possible_names} in dataframe for year {year} for {ticker}")
    return 0

def calculate_profitability(financials, balance_sheet, cash_flow, ticker):
    try:
        years = financials.columns
        if len(years) < 2:
            return 0
        
        latest_year, previous_year = years[0], years[1]
        
        net_income = get_value(financials, ['Net Income', 'Net Income Common Stockholders'], latest_year, ticker)
        net_income_py = get_value(financials, ['Net Income', 'Net Income Common Stockholders'], previous_year, ticker)
        
        op_cf = get_value(cash_flow, ['Operating Cash Flow', 'Cash Flow from Operations'], latest_year, ticker)
        
        total_assets = get_value(balance_sheet, ['Total Assets'], latest_year, ticker)
        total_assets_py = get_value(balance_sheet, ['Total Assets'], previous_year, ticker)
        avg_assets = (total_assets + total_assets_py) / 2 if total_assets and total_assets_py else total_assets or total_assets_py or 1
        
        RoA = net_income / avg_assets if avg_assets else 0
        RoA_py = net_income_py / avg_assets if avg_assets else 0
        
        accruals = op_cf / total_assets - RoA if total_assets else 0
        
        # 
        ni_score = min(net_income / 1e9, 2) if net_income > 0 else 0  # Cap at 2 for net income > $2B
        ni_growth_score = min((net_income - net_income_py) / abs(net_income_py) if net_income_py else 0, 1)
        op_cf_score = min(op_cf / 1e9, 2) if op_cf > 0 else 0  # Cap at 2 for operating cash flow > $2B
        roa_score = min(RoA * 10, 2)  # Cap at 2 for ROA > 20%
        roa_growth_score = min((RoA - RoA_py) * 5, 1)  # Cap at 1 for ROA growth > 20%
        accruals_score = min(accruals * 5, 1) if accruals > 0 else 0
        
        return ni_score + ni_growth_score + op_cf_score + roa_score + roa_growth_score + accruals_score
    
    except Exception as e:
        print(f"Error calculating profitability for {ticker}: {str(e)}")
        return 0

def calculate_leverage(balance_sheet, ticker):
    try:
        latest_year = balance_sheet.columns[0]
        
        lt_debt = get_value(balance_sheet, ['Long Term Debt', 'Total Long Term Debt', 'Net Debt'], latest_year, ticker)
        total_assets = get_value(balance_sheet, ['Total Assets'], latest_year, ticker)
        current_assets = get_value(balance_sheet, ['Total Current Assets', 'Current Assets'], latest_year, ticker)
        current_liab = get_value(balance_sheet, ['Total Current Liabilities', 'Current Liabilities'], latest_year, ticker)
        
        debt_ratio = lt_debt / total_assets if total_assets else 0
        current_ratio = current_assets / current_liab if current_liab else 0
        
        # More granular scoring
        debt_ratio_score = max(0, 2 - debt_ratio * 5)  # 0 score for debt_ratio > 0.4, max 2 for debt_ratio = 0
        current_ratio_score = min(current_ratio - 1, 2) if current_ratio > 1 else 0  # Max 2 for current_ratio > 3
        
        return debt_ratio_score + current_ratio_score
    
    except Exception as e:
        print(f"Error calculating leverage for {ticker}: {str(e)}")
        return 0

def calculate_operating_efficiency(financials, balance_sheet, ticker):
    try:
        years = financials.columns
        if len(years) < 2:
            return 0
        
        latest_year, previous_year = years[0], years[1]
        
        gp = get_value(financials, ['Gross Profit'], latest_year, ticker)
        gp_py = get_value(financials, ['Gross Profit'], previous_year, ticker)
        revenue = get_value(financials, ['Total Revenue', 'Revenue'], latest_year, ticker)
        revenue_py = get_value(financials, ['Total Revenue', 'Revenue'], previous_year, ticker)
        total_assets = get_value(balance_sheet, ['Total Assets'], latest_year, ticker)
        total_assets_py = get_value(balance_sheet, ['Total Assets'], previous_year, ticker)

        gm = gp / revenue if revenue else 0
        gm_py = gp_py / revenue_py if revenue_py else 0
        avg_assets = (total_assets + total_assets_py) / 2 if total_assets and total_assets_py else total_assets or total_assets_py or 1
        at = revenue / avg_assets if avg_assets else 0
        at_py = revenue_py / avg_assets if avg_assets else 0
        
        # More granular scoring
        gm_score = min(gm * 2, 2)  # Max 2 for gross margin > 50%
        gm_growth_score = min((gm - gm_py) * 10, 1)  # Max 1 for 10% growth in gross margin
        at_score = min(at, 2)  # Max 2 for asset turnover >= 2
        at_growth_score = min((at - at_py) * 5, 1)  # Max 1 for 20% growth in asset turnover
        
        return gm_score + gm_growth_score + at_score + at_growth_score
    
    except Exception as e:
        print(f"Error calculating operating efficiency for {ticker}: {str(e)}")
        return 0

summary = pd.DataFrame(columns=['Industry', 'Ticker', 'Company Name', 'PE Ratio', 'Profitability', 'Leverage', 'Operating Efficiency'])

for industry, symbols in tickers.items():
    for ticker in symbols:
        ticker_obj, info, financials, balance_sheet, cash_flow = get_data(ticker)
        
        if ticker_obj is None or info is None or financials is None or balance_sheet is None or cash_flow is None:
            print(f"Skipping {ticker} due to missing data")
            continue
        
        pe_ratio = calculate_pe(info)
        profitability = calculate_profitability(financials, balance_sheet, cash_flow, ticker)
        leverage = calculate_leverage(balance_sheet, ticker)
        operating_efficiency = calculate_operating_efficiency(financials, balance_sheet, ticker)
        
        # Fetch the company name from the info dictionary
        company_name = info.get('longName', ticker)
        
        new_row = {
            'Industry': industry,
            'Ticker': ticker,
            'Company Name': company_name,
            'PE Ratio': pe_ratio,
            'Profitability': profitability,
            'Leverage': leverage,
            'Operating Efficiency': operating_efficiency
        }
        
        summary = summary._append(new_row, ignore_index=True)
        
        print(f"{ticker} ({company_name}) added.")

# Calculate Total Score with weights
weights = {'Profitability': 0.4, 'Leverage': 0.3, 'Operating Efficiency': 0.3}
summary['Total Score'] = (summary['Profitability'] * weights['Profitability'] + 
                          summary['Leverage'] * weights['Leverage'] + 
                          summary['Operating Efficiency'] * weights['Operating Efficiency'])

# Normalize scores
for column in ['Profitability', 'Leverage', 'Operating Efficiency', 'Total Score']:
    max_score = summary[column].max()
    if max_score > 0:
        summary[column] = summary[column] / max_score * 10

# Sort by Total Score
summary = summary.sort_values(by='Total Score', ascending=False)

# Save to CSV
summary.to_csv('Summary_with_names.csv', index=False)
print("Summary file with company names added.")
summary

PANW (Palo Alto Networks, Inc.) added.
CRWD (CrowdStrike Holdings, Inc.) added.
FTNT (Fortinet, Inc.) added.
ETSY (Etsy, Inc.) added.
SHOP (Shopify Inc.) added.
W (Wayfair Inc.) added.
SQ (Block, Inc.) added.
PYPL (PayPal Holdings, Inc.) added.
V (Visa Inc.) added.
TEAM (Atlassian Corporation) added.
Warning: Could not find any of ['Long Term Debt', 'Total Long Term Debt', 'Net Debt'] in dataframe for year 2024-01-31 00:00:00 for ZM
ZM (Zoom Video Communications, Inc.) added.
MSFT (Microsoft Corporation) added.
EA (Electronic Arts Inc.) added.
TTWO (Take-Two Interactive Software, Inc.) added.
AIY.DE (Activision Blizzard Inc) added.
RIVN (Rivian Automotive, Inc.) added.
Warning: Could not find any of ['Long Term Debt', 'Total Long Term Debt', 'Net Debt'] in dataframe for year 2023-12-31 00:00:00 for MBLY
MBLY (Mobileye Global Inc.) added.
VFS (VinFast Auto Ltd.) added.
TSLA (Tesla, Inc.) added.
Summary file with company names added.

	Date	Symbol	Adj Close	Close	High	Low	Open	Volume
0	2010-01-04	MMM	44.016724	69.414719	69.774246	69.122070	69.473244	3640265.0
1	2010-01-05	MMM	43.741020	68.979935	69.590302	68.311035	69.230766	3405012.0
2	2010-01-06	MMM	44.361343	69.958191	70.735786	69.824417	70.133781	6301126.0
3	2010-01-07	MMM	44.393166	70.008362	70.033447	68.662209	69.665550	5346240.0
4	2010-01-08	MMM	44.705982	70.501671	70.501671	69.648827	69.974915	4073337.0

	Date	ticks	Adj Close	Close	High	Low	Open	Volume
0	2019-10-01	MMM	107.833832	132.424744	138.135452	132.249161	137.959869	3912714.0
1	2019-10-02	MMM	105.975113	130.142136	131.454849	129.289291	131.321075	3554871.0
2	2019-10-03	MMM	105.689148	129.790970	129.983276	126.095322	129.715714	3618737.0
3	2019-10-04	MMM	106.090858	130.284286	130.476593	129.096985	129.790970	3165094.0
4	2019-10-07	MMM	104.524887	128.361206	129.891296	128.361206	129.598663	2955555.0
...	...	...	...	...	...	...	...	...
623715	2024-08-28	ZTS	182.910004	182.910004	184.610001	182.100006	182.100006	1485400.0
623716	2024-08-29	ZTS	182.889999	182.889999	184.710007	182.479996	183.910004	1359900.0
623717	2024-08-30	ZTS	183.490005	183.490005	184.220001	180.979996	183.089996	2328500.0
623718	2024-09-03	ZTS	183.570007	183.570007	184.750000	182.460007	182.710007	1680900.0
623719	2024-09-04	ZTS	187.330002	187.330002	187.500000	181.585007	183.190002	1846912.0

	tick	Shortname	Sector	Industry
0	NVDA	NVIDIA Corporation	Technology	Semiconductors
1	TSLA	Tesla, Inc.	Consumer Cyclical	Auto Manufacturers
2	AAPL	Apple Inc.	Technology	Consumer Electronics
3	AMZN	Amazon.com, Inc.	Consumer Cyclical	Internet Retail
4	F	Ford Motor Company	Consumer Cyclical	Auto Manufacturers
5	AMD	Advanced Micro Devices, Inc.	Technology	Semiconductors
6	BAC	Bank of America Corporation	Financial Services	Banks - Diversified
7	T	AT&T Inc.	Communication Services	Telecom Services
8	AAL	American Airlines Group, Inc.	Industrials	Airlines
9	CCL	Carnival Corporation	Consumer Cyclical	Travel Services

	Exchange	Symbol	Shortname	Longname	Sector	Industry	Currentprice	Marketcap	Ebitda	Revenuegrowth	City	State	Country	Fulltimeemployees	Longbusinesssummary	Weight
1	NMS	MSFT	Microsoft Corporation	Microsoft Corporation	Technology	Software - Infrastructure	408.90	3039369887744	1.294330e+11	0.152	Redmond	WA	United States	228000.0	Microsoft Corporation develops and supports so...	0.059346
20	NYQ	ORCL	Oracle Corporation	Oracle Corporation	Technology	Software - Infrastructure	140.75	387887300608	2.122700e+10	0.033	Austin	TX	United States	159000.0	Oracle Corporation offers products and service...	0.007574
28	NMS	ADBE	Adobe Inc.	Adobe Inc.	Technology	Software - Infrastructure	575.25	255065849856	7.844000e+09	0.102	San Jose	CA	United States	29945.0	Adobe Inc., together with its subsidiaries, op...	0.004980
87	NMS	PANW	Palo Alto Networks, Inc.	Palo Alto Networks, Inc.	Technology	Software - Infrastructure	346.15	112533364736	9.601666e+08	0.121	Santa Clara	CA	United States	NaN	Palo Alto Networks, Inc. provides cybersecurit...	0.002197
130	NMS	SNPS	Synopsys, Inc.	Synopsys, Inc.	Technology	Software - Infrastructure	477.53	73355296768	1.652390e+09	0.127	Sunnyvale	CA	United States	20300.0	Synopsys, Inc. provides electronic design auto...	0.001432
149	NMS	CRWD	CrowdStrike Holdings, Inc.	CrowdStrike Holdings, Inc.	Technology	Software - Infrastructure	259.32	63566077952	1.874900e+08	0.317	Austin	TX	United States	9219.0	CrowdStrike Holdings, Inc. provides cybersecur...	0.001241

	ticks	adj close	close	high	low	open	volume	daily return	daily_mean	co_name	Short MA	Long MA	Signal	Cross
date
2022-01-04	CRWD	189.190002	189.190002	197.445007	183.320007	195.889999	6503400.0	-3.420285	191.461254	CrowdStrike Holdings	235.3460	236.554925	0.0	-1.0
2024-09-03	CRWD	266.600006	266.600006	277.510010	265.290009	275.779999	5485800.0	-3.328738	271.295006	CrowdStrike Holdings	299.4642	300.198974	0.0	-1.0

S&P 500 Latest Stocks Analysis¶

TABLE OF CONTENTS

Data Sources¶

Analysis of Most Traded Stocks¶

Key Analyses¶

Comparative Analysis of Top Tech Stocks¶

Fundamental Analysis¶

Data Cleaning¶

Continue with our Work For now on¶

Technical Analysis¶

Trade Volume Analysis¶

Comparitive Analysis of of Top Tech Stocks in the Index¶

Mean Price of Each Stock¶

Volitatliy Analysis¶

Risk Analysis¶

Moving Average¶

Market and Equity Analysis of Stocks¶

Equity ownership analysis¶

Next we are Going to do some Analysis , Selecting do a Side by Side Comparision on Some Direct Alternates of Each Other as¶

Historic EPS and PE Ratio¶

My Approach¶

PE Ratio Insights¶

Fundamental Analysis with Piotroski F-Score¶

Script Description¶

	Top 10 Institutional Holders
Stock Symbol
GOOGL	Date Reported Ho...
MSFT	Date Reported Ho...
WMT	Date Reported Ho...
AMZN	Date Reported Ho...
BABA	Date Reported H...
ORCL	Date Reported ...
SAP	Date Reported Hold...
CRM	Date Reported Holder...

S&P Index Comp	alternate 1	alternate 2	Stock Symbols
Google (Alphabet)	Microsoft		GOOG/GOOGL, MSFT
Walmart	Amazon	Alibaba	WMT, AMZN, BABA
Oracle	SAP	Salesforce	ORCL, SAP, CRM

	Industry	Ticker	Company Name	PE Ratio	Profitability	Leverage	Operating Efficiency	Total Score
11	Software	MSFT	Microsoft Corporation	36.242165	9.050168	4.645531	6.968310	10.0
10	Software	ZM	Zoom Video Communications, Inc.	24.839287	6.530246	10.0	6.540146	9.676711
2	Cybersecurity	FTNT	Fortinet, Inc.	45.828400	8.106195	3.768044	9.122103	9.594741
0	Cybersecurity	PANW	Palo Alto Networks, Inc.	46.056240	10.000000	0.0	7.313343	9.355254
8	Fintech	V	Visa Inc.	29.493034	8.931555	3.302906	6.186266	9.28916
18	Automotive	TSLA	Tesla, Inc.	72.957985	8.225444	6.500295	4.307100	9.261078
12	Gaming	EA	Electronic Arts Inc.	34.035294	7.479406	4.183104	6.801571	8.646036
4	E-commerce	SHOP	Shopify Inc.	80.010100	4.947972	8.986636	6.936685	8.291835
3	E-commerce	ETSY	Etsy, Inc.	26.576742	6.829372	3.023663	7.820137	8.072221
7	Fintech	PYPL	PayPal Holdings, Inc.	18.902912	7.557439	4.255449	3.026382	7.73083
1	Cybersecurity	CRWD	CrowdStrike Holdings, Inc.	100.000000	4.793815	5.512961	8.633508	7.547236
14	Gaming	AIY.DE	Activision Blizzard Inc	0.000000	4.930864	8.351623	3.335013	7.132296
9	Software	TEAM	Atlassian Corporation	0.000000	3.321770	3.082009	10.000000	6.060256
16	Automotive	MBLY	Mobileye Global Inc.	0.000000	1.671184	10.0	3.858088	5.360779
6	Fintech	SQ	Block, Inc.	62.009260	1.686429	5.426277	5.973935	4.511236
5	E-commerce	W	Wayfair Inc.	0.000000	0.755730	0.0	7.544339	2.550857
15	Automotive	RIVN	Rivian Automotive, Inc.	0.000000	-3.399105	6.698802	3.356351	0.43888
13	Gaming	TTWO	Take-Two Interactive Software, Inc.	0.000000	-6.862910	1.870831	3.346543	-3.633858
17	Automotive	VFS	VinFast Auto Ltd.	0.000000	-6.735669	1.908362	2.351831	-3.790046